automatic import of python-mtcnn

3 files changed, 178 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
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+/mtcnn-0.1.1.tar.gz
diff --git a/python-mtcnn.spec b/python-mtcnn.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-mtcnn
+Version:	0.1.1
+Release:	1
+Summary:	Multi-task Cascaded Convolutional Neural Networks for Face Detection, based on TensorFlow
+License:	MIT
+URL:		http://github.com/ipazc/mtcnn
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/ef/11/d549caa104ac2a03b6ef32bfca841ebb04c99ddf704b197c272bcdec054d/mtcnn-0.1.1.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-keras
+Requires:	python3-opencv-python
+
+%description
+The following tables shows the benchmark of this mtcnn implementation running on an `Intel i7-3612QM CPU @ 2.10GHz <https://www.cpubenchmark.net/cpu.php?cpu=Intel+Core+i7-3612QM+%40+2.10GHz>`_, with a **CPU-based** Tensorflow 1.4.1.
+ - Pictures containing a single frontal face:
++------------+--------------+---------------+-----+
+| Image size | Total pixels | Process time  | FPS |
++============+==============+===============+=====+
+| 460x259    | 119,140      | 0.118 seconds | 8.5 |
++------------+--------------+---------------+-----+
+| 561x561    | 314,721      | 0.227 seconds | 4.5 |
++------------+--------------+---------------+-----+
+| 667x1000   | 667,000      | 0.456 seconds | 2.2 |
++------------+--------------+---------------+-----+
+| 1920x1200  | 2,304,000    | 1.093 seconds | 0.9 |
++------------+--------------+---------------+-----+
+| 4799x3599  | 17,271,601   | 8.798 seconds | 0.1 |
++------------+--------------+---------------+-----+
+ - Pictures containing 10 frontal faces:
++------------+--------------+---------------+-----+
+| Image size | Total pixels | Process time  | FPS |
++============+==============+===============+=====+
+| 474x224    | 106,176      | 0.185 seconds | 5.4 |
++------------+--------------+---------------+-----+
+| 736x348    | 256,128      | 0.290 seconds | 3.4 |
++------------+--------------+---------------+-----+
+| 2100x994   | 2,087,400    | 1.286 seconds | 0.7 |
++------------+--------------+---------------+-----+
+MODEL
+#####
+By default the MTCNN bundles a face detection weights model.
+The model is adapted from the Facenet's MTCNN implementation, merged in a single file located inside the folder 'data' relative
+to the module's path. It can be overriden by injecting it into the MTCNN() constructor during instantiation.
+The model must be numpy-based containing the 3 main keys "pnet", "rnet" and "onet", having each of them the weights of each of the layers of the network.
+For more reference about the network definition, take a close look at the paper from *Zhang et al. (2016)* [ZHANG2016]_.
+LICENSE
+#######
+`MIT License`_.
+
+%package -n python3-mtcnn
+Summary:	Multi-task Cascaded Convolutional Neural Networks for Face Detection, based on TensorFlow
+Provides:	python-mtcnn
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-mtcnn
+The following tables shows the benchmark of this mtcnn implementation running on an `Intel i7-3612QM CPU @ 2.10GHz <https://www.cpubenchmark.net/cpu.php?cpu=Intel+Core+i7-3612QM+%40+2.10GHz>`_, with a **CPU-based** Tensorflow 1.4.1.
+ - Pictures containing a single frontal face:
++------------+--------------+---------------+-----+
+| Image size | Total pixels | Process time  | FPS |
++============+==============+===============+=====+
+| 460x259    | 119,140      | 0.118 seconds | 8.5 |
++------------+--------------+---------------+-----+
+| 561x561    | 314,721      | 0.227 seconds | 4.5 |
++------------+--------------+---------------+-----+
+| 667x1000   | 667,000      | 0.456 seconds | 2.2 |
++------------+--------------+---------------+-----+
+| 1920x1200  | 2,304,000    | 1.093 seconds | 0.9 |
++------------+--------------+---------------+-----+
+| 4799x3599  | 17,271,601   | 8.798 seconds | 0.1 |
++------------+--------------+---------------+-----+
+ - Pictures containing 10 frontal faces:
++------------+--------------+---------------+-----+
+| Image size | Total pixels | Process time  | FPS |
++============+==============+===============+=====+
+| 474x224    | 106,176      | 0.185 seconds | 5.4 |
++------------+--------------+---------------+-----+
+| 736x348    | 256,128      | 0.290 seconds | 3.4 |
++------------+--------------+---------------+-----+
+| 2100x994   | 2,087,400    | 1.286 seconds | 0.7 |
++------------+--------------+---------------+-----+
+MODEL
+#####
+By default the MTCNN bundles a face detection weights model.
+The model is adapted from the Facenet's MTCNN implementation, merged in a single file located inside the folder 'data' relative
+to the module's path. It can be overriden by injecting it into the MTCNN() constructor during instantiation.
+The model must be numpy-based containing the 3 main keys "pnet", "rnet" and "onet", having each of them the weights of each of the layers of the network.
+For more reference about the network definition, take a close look at the paper from *Zhang et al. (2016)* [ZHANG2016]_.
+LICENSE
+#######
+`MIT License`_.
+
+%package help
+Summary:	Development documents and examples for mtcnn
+Provides:	python3-mtcnn-doc
+%description help
+The following tables shows the benchmark of this mtcnn implementation running on an `Intel i7-3612QM CPU @ 2.10GHz <https://www.cpubenchmark.net/cpu.php?cpu=Intel+Core+i7-3612QM+%40+2.10GHz>`_, with a **CPU-based** Tensorflow 1.4.1.
+ - Pictures containing a single frontal face:
++------------+--------------+---------------+-----+
+| Image size | Total pixels | Process time  | FPS |
++============+==============+===============+=====+
+| 460x259    | 119,140      | 0.118 seconds | 8.5 |
++------------+--------------+---------------+-----+
+| 561x561    | 314,721      | 0.227 seconds | 4.5 |
++------------+--------------+---------------+-----+
+| 667x1000   | 667,000      | 0.456 seconds | 2.2 |
++------------+--------------+---------------+-----+
+| 1920x1200  | 2,304,000    | 1.093 seconds | 0.9 |
++------------+--------------+---------------+-----+
+| 4799x3599  | 17,271,601   | 8.798 seconds | 0.1 |
++------------+--------------+---------------+-----+
+ - Pictures containing 10 frontal faces:
++------------+--------------+---------------+-----+
+| Image size | Total pixels | Process time  | FPS |
++============+==============+===============+=====+
+| 474x224    | 106,176      | 0.185 seconds | 5.4 |
++------------+--------------+---------------+-----+
+| 736x348    | 256,128      | 0.290 seconds | 3.4 |
++------------+--------------+---------------+-----+
+| 2100x994   | 2,087,400    | 1.286 seconds | 0.7 |
++------------+--------------+---------------+-----+
+MODEL
+#####
+By default the MTCNN bundles a face detection weights model.
+The model is adapted from the Facenet's MTCNN implementation, merged in a single file located inside the folder 'data' relative
+to the module's path. It can be overriden by injecting it into the MTCNN() constructor during instantiation.
+The model must be numpy-based containing the 3 main keys "pnet", "rnet" and "onet", having each of them the weights of each of the layers of the network.
+For more reference about the network definition, take a close look at the paper from *Zhang et al. (2016)* [ZHANG2016]_.
+LICENSE
+#######
+`MIT License`_.
+
+%prep
+%autosetup -n mtcnn-0.1.1
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-mtcnn -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.1-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..513ba61
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+bea6a6cd819651d77ea0793575efb7f9  mtcnn-0.1.1.tar.gz